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Smoothing Spline ANOVA Models: R Package gss

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  • Gu, Chong

Abstract

This document provides a brief introduction to the R package gss for nonparametric statistical modeling in a variety of problem settings including regression, density estimation, and hazard estimation. Functional ANOVA (analysis of variance) decompositions are built into models on product domains, and modeling and inferential tools are provided for tasks such as interval estimates, the “testing” of negligible model terms, the handling of correlated data, etc. The methodological background is outlined, and data analysis is illustrated using real-data examples.

Suggested Citation

  • Gu, Chong, 2014. "Smoothing Spline ANOVA Models: R Package gss," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i05).
    • Handle: RePEc:jss:jstsof:v:058:i05
    DOI: http://hdl.handle.net/10.18637/jss.v058.i05
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    References listed on IDEAS

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    1. Young‐Ju Kim & Chong Gu, 2004. "Smoothing spline Gaussian regression: more scalable computation via efficient approximation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(2), pages 337-356, May.
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    Cited by:

    1. Hugo Maruri-Aguilar & Henry Wynn, 2023. "Sparse polynomial prediction," Statistical Papers, Springer, vol. 64(4), pages 1233-1249, August.
    2. Federico Ferraccioli & Eleonora Arnone & Livio Finos & James O. Ramsay & Laura M. Sangalli, 2021. "Nonparametric density estimation over complicated domains," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(2), pages 346-368, April.
    3. Dimitrova, Dimitrina S. & Kaishev, Vladimir K. & Lattuada, Andrea & Verrall, Richard J., 2023. "Geometrically designed variable knot splines in generalized (non-)linear models," Applied Mathematics and Computation, Elsevier, vol. 436(C).

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